Total 78 Posts

## Sentiment Analysis in Python With TextBlob

### Introduction

State-of-the-art technologies in NLP allow us to analyze natural languages on different layers: from simple segmentation of textual information to more sophisticated methods of sentiment categorizations.

However, it does not inevitably mean that you should be highly advanced in programming to implement high-level tasks such as sentiment analysis in

## Simple NLP in Python With TextBlob: Tokenization

### Introduction

The amount of textual data on the Internet has significantly increased in the past decades. There's no doubt that the processing of this amount of information must be automated, and the TextBlob package is one of the fairly simple ways to perform NLP - Natural Language Processing.

It provides

## Facial Detection in Python with OpenCV

### Introduction

Facial detection is a powerful and common use-case of Machine Learning. It can be used to automatize manual tasks such as school attendance and law enforcement. In the other hand, it can be used for biometric authorization.

## Kernel Density Estimation in Python Using Scikit-Learn

### Introduction

This article is an introduction to kernel density estimation using Python's machine learning library scikit-learn.

Kernel density estimation (KDE) is a non-parametric method for estimating the probability density function of a given random variable. It is also referred to by its traditional name, the Parzen-Rosenblatt Window method, after its

## Deep Learning in Keras - Building a Deep Learning Model

### Introduction

Deep learning is one of the most interesting and promising areas of artificial intelligence (AI) and machine learning currently. With great advances in technology and algorithms in recent years, deep learning has opened the door to a new era of AI applications.

In many of these applications, deep learning

## Translating Strings in Python with TextBlob

### Introduction

Text translation is a difficult computer problem that gets better and easier to solve every year. Big companies like Google are actively working on improving their text translation services which enables the rest of us to use them freely.

Apart from their great personal use, these services can be

## Deep Learning in Keras - Data Preprocessing

### Introduction

Deep learning is one of the most interesting and promising areas of artificial intelligence (AI) and machine learning currently. With great advances in technology and algorithms in recent years, deep learning has opened the door to a new era of AI applications.

In many of these applications, deep learning

## Deep Learning Models in Keras - Exploratory Data Analysis (EDA)

### Introduction

Deep learning is one of the most interesting and promising areas of artificial intelligence (AI) and machine learning currently. With great advances in technology and algorithms in recent years, deep learning has opened the door to a new era of AI applications.

In many of these applications, deep learning